Plant production feedback control loop optimization

ABSTRACT

Sensors provide environmental conditions and plant growth information while control devices regulate the conditions. A calculated data point for a growth of a plant may be generated, and an optimum input variable value for the growth may be obtained. A target value for a sensor data point of a sensor that monitors a condition affecting the growth or the calculated data point for the growth may be further acquired. As such, one or more control devices that most strongly correlate with the target value may be determined based on at least one of the optimum input variable value or a vector association array. Thus, at least one control device setting value for the one or more control devices may be ascertained based on a target path for achieving the target value. Accordingly, each control device may be commanded to implement a control device setting value.

CROSS REFERENCE TO RELATED PATENT APPLICATION

This patent application claims priority to U.S. Provisional ApplicationNo. 62/030,466, entitled “Optimization of Plant Production throughFeedback-Control Loop Method and System”, filed on Jul. 29, 2014, whichis hereby incorporated in its entirety by reference.

BACKGROUND

Typical greenhouses measure environmental conditions with imprecise,analog, one high and one low data point per day. Measurements areusually collected by walking around and looking at the measurementequipment. A typical greenhouse may have dozens or hundreds of differentswitches, timers and assorted controls spread out inconveniently andinefficiently throughout the greenhouse. Generally, gardeningexperiments are conducted over weeks or months using side by side,single variable variations against a control group. However, suchgardens may not be equipped for precise control or data collection. Thislack of precise control and data collect may lead to prolongedexperiments using a multitude of ad hoc implicit assumptions. Forexample, the optimization of plant growth for just two variables, suchas CO₂ level and temperature, may take months of experimentation.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures, in which the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 illustrates an example architecture of computing devices forimplementing a plant production feedback control loop.

FIG. 2 is a block diagram of an exemplary server for providing a plantproduction feedback control loop.

FIG. 3 is a block diagram of a data store for storing data associatedwith the provision of a plant production feedback control loop.

FIG. 4 is a flow diagram of an example process for implementing a maincontroller routine of a plant production feedback control loop.

FIG. 5 is a flow diagram of an example process for implementing ameasurement routine of a plant production feedback control loop.

FIG. 6 is a flow diagram of an example processing for implementing afeedback analysis routine of a plant production feedback control loop.

FIG. 7 is a flow diagram of an example process for implementing a devicecontrol routine of a plant production feedback control loop.

FIG. 8 is a flow diagram of an example process for implementing a limitmaintainer routine of a plant production feedback control loop.

FIG. 9 is a flow diagram of an example process for implementing a userprogram generation routine of a plant production feedback control loop.

DETAILED DESCRIPTION

This disclosure provides specific details for an understanding ofvarious examples of the technology. One skilled in the art willunderstand that the technology may be practiced without many of thesedetails. In some instances, structures and functions have not been shownor described in detail or at all to avoid unnecessarily obscuring thedescription of the examples of the technology. It is intended that theterminology used in the description presented below be interpreted inits broadest reasonable manner, even though it is being used inconjunction with a detailed description of certain examples of thetechnology. Although certain terms may be emphasized below, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the term “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words, “herein,” “above,”“below,” and words of similar import, when used in this application,shall refer to this application as a whole and not to particularportions of this application. When the context permits, words using thesingular may also include the plural while words using the plural mayalso include the singular. The word “or,” in reference to a list of twoor more items, covers all of the following interpretations of the word:any of the items in the list, all of the items in the list, and anycombination of one or more of the items in the list.

Multiple instances of certain components are labeled with an elementnumber and letter; all such component instances are equivalent withinnormal ranges. Multiple instances of otherwise identical components cancontrol, be controlled, or communicate separately through assignment ofunique or distinguishing identifiers. Such components may be referred toherein only by element number, without a letter in conjunctiontherewith, in which case the reference is to any of such components.

As used herein, a “plant” may be at least one of a flowering plant,conifer and other gymnosperm, fern, clubmoss, hornwort, liverwort, moss,green algae, red and brown algae, fungi, archaea, and bacteria (which isa broader definition than commonly applies to just green plants). Asused herein, a “room” (including Room 100) is an enclosure, tent, room,or the like. As used herein, a “site” (including Site 101) is an areaequal to or larger than a room, such as a building, location, realproperty, or the like, containing one or more rooms.

Example Architecture

FIG. 1 illustrates an example network architecture for performingmachine learning-based geolocation and hotspot area identification. Aserver 200 is in communication with a server data store 300 via anetwork 199. Additionally, the server 200 is further in communicationwith multiple devices via the network 199. The network 199 may comprisescomputers, network connections among the computers, and softwareroutines to enable communication between the computers over etworkconnections. Examples of Network 199 comprise an Ethernet network, theInternet, and/or a wireless network, such as a GSM, TDMA, CDMA, EDGE,HSPA, LTE, LTE-Advanced or other network provided by a wireless serviceprovider. Connection to Network 199 may be via a wireless or wirelineconnection. More than one network may be involved in a communicationsession between the multiple devices. In some embodiments, a computermay execute software routes that include the layers of the Open SystemsInterconnection (OSI) model of computer networking to connect to thenetwork 199.

The multiple devices may include control devices, such as controldevices 1-N, which are collectively referred to as control devices 105.The multiple components may further include various sensors, such assensors 1-N, which are collectively referred to as sensors 110. Themultiple devices may additionally include an artificial light 120, userdevice 130, and a third party computer 125. In some instances, theartificial light 120 may be a type of the control devices 105. Themultiple devices may communicate directly with each other or via thenetwork 199 without communicating with the server 200. The controldevices 105, the sensors 110, and the artificial light 120 may belocated in a room 100 that is located on a site 101. Additionally, aplant 115 may be located in the room 100.

The server data store 300 may include logical organizational structuresthat store data, such as relational databases, object databases,object-relational databases, and/or key-value databases. Accordingly,the server data store 300 may store multiple records, including Siterecord 305, room record 310, user record 390, and/or the like. Thisdisclosure may discuss a first computer or computer process asconnecting to a second computer or computer process. For example, thesensors 110, the control devices 105, the user device 130 or the thirdparty computer 125 may connect to the server 200 or to a correspondingdata store, such as the server data store 300. It will be appreciatedthat such connections may be made to, using, or via other components.For example, a statement that the sensor 110 may connects with or sendsdata to the server 200 should be understood as saying that sensor 110may connect with or send data to the server data store 300. Referencesherein to a “database” should be understood as equivalent to a “datastore” and vice versa. Although the computers or the databases mayillustrated as components integrated in one physical unit, the computersor the databases may be provided by common (or separate) physicalhardware and common (or separate) logic processors and memorycomponents. Though discussed as being executed by or within onecomputing device, the software routines and data groups used by thesoftware routines may be stored and/or executed remotely relative to anyof the computers through, for example, application virtualization.

The server 200 may obtain and/or receive environmental conditions andother sensor information from the sensors 110 via, for example,measurement routine 500. Each of the sensor 110 may be an electrical orelectro-mechanical device with simple output or may be a more complexcomputer with an independent operating system, user interface, and aninterface for interacting with other devices and computers. Anon-exhaustive list of sensors 110 examples may include an airtemperature thermometer, a soil temperature thermometer, a liquidtemperature thermometer, a remote IR temperature sensor (such as tomeasure leaf temperature), an atmospheric humidity sensor, a soilmoisture sensor (such as hygrometer), a CO₂ sensor, a gas compositionsensor (propane, smoke, and the like). The examples may further includea camera, a light level and color sensor, a pH sensor (for soil and/orliquid), a wind speed and direction sensor, an air pressure sensor, amotion sensor (which may be mechanical, acoustic, or an optical, forsensing plant motion or other motion), flow meters or sensors, positionsensors that utilizing global position system (GPS), radio frequency(RF) identification tags attached to plants, and/or the like. Theexamples may additionally include a sound or acoustic sensor, a liquidlevel sensor. The sound or acoustic sensor may include a conventionalmicrophone, a high-gain microphone recording capillary activity, orultrasonic acoustic sensors for range finding and root mas sensing.Other examples may include magnetic sensors (such as a compass, a HallEffect sensor, etc.,), acceleration sensors, tilt or flex sensors (suchas on branches of a plant to measure bending), contact sensors,electromagnetic field (EMF) and other radiation (ionizing ornonionizing) sensors, a circuit sensor (including a circuit breakersensor), a scale, and/or the like. In some embodiments, some of thesensors 110 may also serve as control devices, such as the controldevices 105.

Database records from the sensors 110 may be stored in the server datastore 300 as sensor record 335. Additionally or alternatively, databaserecords containing information from the sensors 110 may be stored in,for example, as sensor data point records 340, in which each record maybe provided with a date-time stamp.

The server 200 may control environmental conditions in the room 100and/or the site 101. The environmental conditions may be controlled viadevice control routine 700 that are implemented by the control devices105. A non-exhaustive list of control device types may include thefollowing: a relay, a solenoid, motor, and a dimmer. Other examples oftypes of control devices 105 may include a CO₂ emitter or absorber, avalve (whether used for a liquid or a gas, water or another a pHsolution, or the like), an artificial light (such as the artificiallight 120) or other source of electromagnetic radiation, a louver, aheater, an air conditioner, a fan, a humidifier or dehumidifier, anactuator (such as controlling tilt or pan of a camera, a camera arm, amechanical belt, a tray, a rotating or slider arm, and/or the like), anacoustic emitter, a switch, a circuit breaker, a liquid pump, a gaspump, and/or the like. Database records regarding types of controldevices 105 may be stored in the server data store 300 as records ofcontrol device type 325.

Accordingly, the control devices 105 may be classified according tocontrol device type 325, and each control device may be controlled witha command of the corresponding control device type to the controldevice. For example, in instances in which the control device is theartificial light 120, the artificial light 120 may be controlled so thatthe light spectrum and/or light level emitted by the artificial light120 is modified to produce a desired response in a plant's metabolicrate, i.e., relative photosynthesis. The modification of the brightnessof the light generated by the artificial light 120 may involve changinga distance between the artificial light 120 and the plant, the use of adimmer, the use of a louver to partially block the emitted light, and/orso forth. The change in distance may be accomplished using an actuator,a motor, and/or the like. The modification of the light spectrum of theartificial light 120 may involve changing the input voltage and/orcurrent of the artificial light 120, the automatic deployment ofspectrum filters to filter the light emitted from the artificial light120, and/or so forth. In at least one embodiment, some of the controldevices 105 may also serve as sensors.

In some instances, the user device 130 may directly send commands to acontrol device via device control routine 700. In other instances, theuser device may send commands to a control device via an API call to thedevice control routine 700 as stored on the server 200. In the latterinstances, the control device may reports its status to the server 200and/or the device control routine 700). Examples of control commands mayinclude: circuit_on(Control Device IP, circuit #); water_on(room, valve#), circuit_on(control device IP, valve circuit). The commands for acontrol device may be set with an expiration timer and a priority level.

Database records regarding the control devices 105 may be stored in theserver data store 300 as control device record 320. Further, databaserecords containing information regarding settings for the controldevices 105 may be stored in, for example, records of control devicedata point 330, in which each record may be provided with a date-timestamp.

The user device 130 may be a smart phone, a mobile phone, a tabletcomputer, a laptop computer, a desktop computer, a wearable computer,and/or the like. Users may use the user device 130 illustrates tointeract with the server 200, the control devices 105, the sensors 110,and/or the third party computer 125. Database records containinginformation regarding the user device 130 and users of user device 130may be stored as user record 390. The database record for a user maycontain login credentials and account information of a user. The thirdparty computer 125 may be a computer that is operated by variousentities, such as a social media service.

Example Server Components

FIG. 2 is a block diagram of an exemplary server for providing a plantproduction feedback control loop. The server 200 may comprises one ormore processing unit 210, memory 250, display component 240 and inputcomponent 245, all interconnected along with network interface 230 viabus 220. Processing unit 210 may comprise one or more general-purposeCentral Processing Units (CPU) 212 as well as one or morespecial-purpose Graphics Processing Units (GPU) 214. The components ofprocessing unit 210 may be utilized by operating system 255 fordifferent functions required by routines executed by the Server 200,such as the main controller routine 400, the measurement routine 500,the feedback analysis routine 600, the device control routine 700, limitmaintainer routine 800, the user program generation routine 900, theuser interface routine 260, and application program interface (API) 265.The following routines may be subroutines of the main controller routine400 or may be executed independently: the measurement routine 500, thefeedback analysis routine 600, the device control routine 700, and thelimit maintainer routine 800.

The network interface 230 may be utilized to form connections with thenetwork 199 or to form device-to-device connections with othercomputers. The memory 250 may comprise random access memory (RAM), readonly memory (ROM), and a permanent mass storage device, such as a diskdrive or synchronous dynamic random-access memory (SDRAM). The memory250 may store program code for software routines, such as, for example,the main controller routine 400, the measurement routine 500, thefeedback analysis routine 600, the device control routine 700, the limitmaintainer routine 800, the user program generation routine 900, theuser interface routine 260, and the API 265.

The memory 250 may also store program code for browsers, email clients,server applications, client applications, and database applications.Additional data groups for routines, such as for a web server and webbrowser, may also be present on and executed by the server 200. Webserver and browser routines may provide an interface for interactingwith the other computing devices through web server and web browserroutines. The web server may serve and provide data and information inthe form of webpages and HyperText Markup Language (HTML) documents orfiles. The browsers and web servers are meant to illustrate user andmachine interface routines generally, and may be replaced by equivalentroutines for serving and rendering information to a computing device andin an interface in a computing device. In addition, the memory 250 mayalso store the operating system 255.

These software components may be loaded from a non-transitory computerreadable storage medium 295 into the memory 250 of the computing deviceusing a drive mechanism associated with the non-transitory computerreadable storage medium 295. The non-transitory computer readablestorage medium 295 may be a floppy disc, tape, a DVD/CD-ROM drive, amemory card, or other like storage medium. Alternatively orconcurrently, the software components may be loaded via a mechanismother than a drive mechanism and computer readable storage medium 295.For example, the software components may be loaded via the networkinterface 230.

The server 200 may also comprise hardware supporting input modalities.For example, the input components 245 may include a touchscreen, acamera 1205, a keyboard, a mouse, a trackball, a stylus, motiondetectors, and a microphone. The Input components 245 may also include atouchscreen display, in which the touchscreen display may respond toinput in the form of contact by a finger or stylus with a surface of thetouch screen. In some embodiments, the input component 245 and thedisplay component 240 may be an integral part of the Server 200. Inother embodiments, the input component 245 and the display component 240may be components of another device.

The server 200 may further include the bus 220 for communicating withthe server data store 300. In various embodiments, Bus 220 may comprisea storage area network (SAN), a high speed serial bus, and/or via othersuitable communication technology. The server 200 may communicate withthe server data store 300 via the network interface 230. The server 200may, in some embodiments, include many more components than those shownin FIG. 2. However, it is not necessary that all of these generallyconventional components be shown in order to disclose an illustrativeembodiment.

The user interface routine 260 may provide a dashboard to the userdevice 130 with column charts for sites, rooms, and/or plants. Thedashboard may present temperature, color coded status, such asblue/transparent for offline, green for operational and in limits,yellow for operational and at/near boundary conditions, red for out oflimits. The column charts may hyperlink to details and additionalinformation. The dashboard may provide a sweep counter indicating thetime since that last data was collected. The charts and information maybe organized by different time intervals, such as the last 24 hours, thelast 48 hours, the last week, and the like.

The dashboard may provide device control interfaces to report and changethe settings of the control devices 105 and to report and change one ormore targets 365 with respect to the plant 115. The dashboard mayprovide interfaces to report the status of the user program 395 andchange the user program 395. The user interface routine 260 may utilizethe user record 390 for account log-in authentication and access controlvia user authentication credentials. For example, the authenticationcredentials may include credentials for performing face and voicerecognition.

FIG. 3 is a block diagram of a data store for storing data associatedwith the provision of a plant production feedback control loop. Theillustrated components of server data store 300 are data groups used byroutines and are discussed further herein in the discussion of other ofthe figures. The data groups used by routines illustrated in FIG. 3 maybe represented by a cell in a column or a value separated from othervalues in a defined structure in a digital document or file. Thoughreferred to herein as individual records or entries, the records maycomprise more than one database entry. The database entries may be,represent, or encode numbers, numerical operators, binary values,logical values, text, string operators, joins, conditional logic, tests,and similar.

Example Processes

FIGS. 4-9 present illustrative processes 400-900 for implementing aplant production feedback control loop. Each of the processes 400-900 isillustrated as a collection of blocks in a logical flow chart, whichrepresents a sequence of operations that can be implemented in hardware,software, or a combination thereof. In the context of software, theblocks represent computer-executable instructions that, when executed byone or more processors, perform the recited operations. Generally,computer-executable instructions may include routines, programs,objects, components, data structures, and the like that performparticular functions or implement particular abstract data types. Theorder in which the operations are described is not intended to beconstrued as a limitation, and any number of the described blocks can becombined in any order and/or in parallel to implement the process. Fordiscussion purposes, the processes 400-900 are described with referenceto FIG. 1.

FIG. 4 is a flow diagram of an example process for implementing a maincontroller routine 400 of a plant production feedback control loop. Themain controller routine 400 may be executed relative to plant 115, room100, and/or site 101. At block 500, the main controller routine 400 mayexecute the measurement routine 500 to access or obtain measurements inthe form of sensor data points 340 from the sensors 110. The sensor datapoints 340 may be used to determine calculated data points 345 accordingto a calculation formula 350. The measurement routine 500 is discussedfurther in relation to FIG. 5.

At block 600, the main controller routine 400 may execute the feedbackanalysis routine 600 to analyze the sensor data points 340, thecalculated data points 345 and the control device data point 330. Thecalculations may be performed across a time interval 355 to develop anoptimum input variable 370 and a vector association array 380. Thefeedback analysis routine 600 is discussed further in relation to FIG.6.

At block 405, the main controller routine 400 may obtain a target 365.The target 365 may be a desired or targeted sensor data point 340 and/orthe calculated data point 345. The target 365 may be obtained from apreset catalog provided by an operator of main controller routine 400,from a user, such as via a user interface, via an API (a machineinterface), or via the user program 395. For example, a value of thetarget 365 may be a moisture, temperature, or light level or may be anoutcome, such as maximum total photosynthesis, a plant growth rate, aplant metabolic rate, i.e., relative photosynthesis, or an esterproduction goal. The target 365 may apply for a time period that isspecified relative to a calendar or for a time period that occursrelative to sensor data point 340 and/or calculated data point 345.

At block 410, the main controller routine 400 may obtain a controldevice that is most strongly associated with the target 365. The controldevice may be obtained from the optimum input variable 370, the vectorassociation array 380, as specified by the operator of main controllerroutine 400, and/or by a user, such as via a user interface, an API, orthe user program 395. At block 415, the main controller routine 400 maydetermine setting values for the control device that are calculated toachieve the target 365. For example, a setting value for a controldevice in the form of the artificial light 120 may be a distance of thelight from the artificial light 120, a louver setting for a louver thatpartially blocks light emission from the artificial light 120, an inputvoltage or current to the artificial light 120, a light filter settingfor the artificial light 120, or another control device setting valuethat modifies the brightness and/or spectrum of the light that isemitted by the artificial light 120. The setting values may be saved asthe target path 385. This determination may be made with reference tothe optimum input variable 370, the vector association array 380, asspecified by the operator of main controller routine 400, and/or asspecified by a user via a user interface, an API, or the user program395.

At block 700, the main controller routine 400 may execute the devicecontrol routine 700 to implement the target path 385 determined at block415. The device control routine 700 is discussed further in relation toFIG. 7. At block 800, the main controller routine 400 may execute thelimit maintainer routine 800 to maintain the sensor data point 340within the range 375. The limit maintainer routine 800 is discussedfurther in relation to FIG. 8. At block 499, the main controller routine400 may conclude, return to a starting state, and/or return to a processthat spawned the main controller routine 400.

FIG. 5 is a flow diagram of an example process for implementing themeasurement routine 500 of a plant production feedback control loop. Atblock 505, the measurement routine 500 may initiate an iteration ofblocks 510 to 520 for each sensor 110 relative to the room 100 and/orthe site 101. At block 510, the measurement routine 500 may obtain orreceive a measurement value from the sensor 110. At block 515, themeasurement routine 500 may store the value obtained or received atblock 510 as sensor data point 340. At block 525, the measurementroutine 500 may initiate an iteration of blocks 530 to 545 for eachcalculated data point 345, which may be performed relative to the plant115, room 100, and/or the site 101.

At block 530, the calculation formula 350, the corresponding sensor datapoint 340, or the calculated data point 345 used in the calculationformula 350, may be obtained. The calculation formula 350 may be aformula that determines information of interest. For instance,photosynthesis in plant 115 or an area of plant 115 may be determined byperforming a Normalized Difference Vegetation Index (NDVI) calculationon near-infrared and visible spectrum images obtained from the values ofthe sensor data points 340. In one example, the color of the plant 115or an area of the plant 115 may be determined by obtaining an image ofplant 115 from the values of the sensor data point 340, and deriving thepixel color of an area in the image for the values of the sensor datapoint 340. In some scenarios, the pixel color that is derived may berelative to a histogram of the image.

In other instances, an area of interest on the plant 115 or in the room100 may be determined through optical image recognition, throughidentification of an area in an image by a user, through identificationof an area in an image proximate to a tag (which may be identifiedthrough optical image recognition), and/or through an identification ofa pan or tilt of a camera. For example, optical image recognition ofstem and/or flower nodes on Plant 115 may be performed. Accordingly, thephysical growth of plant 115 (such as by weight or from images oroptical recognition performed relative to images) may be observed, suchthat any change in such growth value from a corresponding previous valuemay be calculated.

In another example, the extent of canopy of the plant 115 may bedetermined from images, such that a change in the canopy growth valuefrom a corresponding previous value may be calculated. In anotherexample, the maturity of the plant 115 may be determined from images,such that a change in the maturity value from a corresponding previousvalue may be calculated. In yet another example, the amount of water inthe plant 115 and/or the room 100 may be determined, such that a changein the amount of water from a corresponding previous amount may becalculated. In a further example, the weight of the plant 115 may bedetermined, such that a change in the weight from a correspondingprevious weight may be calculated.

At block 535, a corresponding calculation Formula 350 is executed withthe values of the sensor data point 340. At block 540, the calculatedvalue is stored as the calculated data point 345. At block 599, themeasurement routine 500 may conclude, return to a starting state, and/orreturn to a process that spawned the measurement routine 500.

FIG. 6 is a flow diagram of an example processing for implementing thefeedback analysis routine 600 of a plant production feedback controlloop. At block 605, the feedback analysis routine 600 may initiate aniteration of blocks 610 to 675 for each output variable that is to beanalyzed. For example, the output variables may include one or moresensor data point 340 or calculated data point 345. At block 610, thefeedback analysis routine may initiate an iteration of blocks 615 to 670for each input variable to be analyzed. For example, the input variablesmay include one or more control device data point 330. At block 615, thefeedback analysis routine 600 may initiate an iteration of blocks 620 to665 for each range of input variable of block 610 that is to beanalyzed, such as the range 375. At block 620, the feedback analysisroutine 600 may initiate an iteration of blocks 625 to 660 for each timeinterval that is desired to be analyzed, such as the time interval 355.

At block 625, the feedback analysis routine 600 may obtain or receivethe sensor data point 340 or the calculated data point 345. At block630, the feedback analysis routine 600 may initiate an iteration ofblocks 635 to 655 for each control variable in the set of inputvariables of blocks 610 to 670. At block 635, the feedback analysisroutine 600 may use a brute force or machine learning algorithm todetermine the one or more values for at least one control input variablethat produce the maximum desired output variable in the set of desiredoutput variables of blocks 605 to 675.

The brute force or proof by exhaustion algorithm may be used when alimited number of cases are to be considered and/or when there is alonger duration allowed for performing the algorithm. The machinelearning algorithm may be utilized when there are a large number ofcases or a limited amount of time for performing the algorithm. Types ofmachine learning algorithms which may be utilized include, for example,decision tree learning, association rule learning, artificial neuralnetworks, inductive logic, support vector machines, clustering, Bayesiannetworks, reinforcement learning, representation learning, similarityand metric learning, and sparse dictionary learning. The foregoing maybe considered single variable optimization, N-variable optimization, orN-dimensional (within limits) vector analysis of variable relationships.

At block 640, the control input variable value that produces the maximumdesired output of block 635 may be stored as the optimum input variable370. At block 645, the direction response between the input variable anddesired maximum output may be determined or obtained. The maximum outputmay be a desired outcome. For example, a maximum output value may be amaximum plant yield, a particular plant color, a specific plant flavor,or another quantifiable plant growth outcome. In other examples, amaximum output value may be the maximum amount of water saved duringplant growth, the maximum amount of fertilizer saved during plantgrowth, or another quantifiable resource use minimization outcome. Inturn, the value of the optimum input variable 370 may be calculated toachieve the desired outcome regardless of the desired outcome type. Forexample, the optimum input variable 370 may be a specific light level, aspecific light spectrum or a range of light spectrums, and/or so forth.At block 650, the optimum input variable 370 of block 640 and thedetermined directional response of block 645 may be stored in a vectorassociation array as the vector association array 380. In blocks 655 toblock 675, as a part of performing block 650, a desired output may bestored for each input variable (block 655), for a time interval (block660), for a range of input variables (block 665), for each set of inputvariables (block 670), and for each desired output variable (block 675).At block 699, the feedback analysis routine 600 may conclude, may returnto a starting state, and/or return to a process that spawned thefeedback analysis routine 600.

FIG. 7 is a flow diagram of an example process for implementing thedevice control routine 700 of a plant production feedback control loop.At block 705, the device control routine 700 may receive one or moresetting values for one or more control devices 105. At block 710, thedevice control routine may initiate an iteration of blocks 715 to 755for each of the control devices 105. At block 715, the device controlroutine 700 may obtain the type of a control device 105, for example,from the record of the control device type 325.

At block 720, the device control routine 700 may initiate an iterationof blocks 725 to 750 for each control device setting value. At block725, the control device setting value may be set by the device controlroutine 700 making a shell call to the control device 105 with thesetting value. At block 730, a determination may be made regardingwhether or not a value is received from the control device 105 at block725. If affirmative, then a determination may be made at block 735regarding whether or not the received value is expected. If the receivedvalue is expected, then the received value may be stored as the controldevice data point 330 at block 745. If negative at block 730 and/or theblock 735, then an error or equivalent may be flagged at block 740.

At block 745, the error or the setting value of block 725 may be storedas the control device data point 330. At block 799, the device controlroutine 700 may conclude, return to a starting state, and/or return to aprocess that spawned the device control routine 700.

FIG. 8 is a flow diagram of an example process for implementing thelimit maintainer routine 800 of a plant production feedback controlloop. The limit maintainer routine 800 may maintain environmentalconditions in the room 100 or otherwise for the plant 115 within arange. Accordingly, the main control routine 400 and the user program395 may operate without accidentally exceed environmental conditions forthe plant 115.

At block 805, the limit maintainer routine 800 may initiate an iterationof blocks 810 to 850 for each sensor data point 340 or the calculateddata point 345 that is being monitored. The monitored sensor data point340 or the calculated data point 345 may be specified by the userprogram 395. Alternatively, the monitored sensor data point 340 or thecalculated data point 345 may be a part of a maintenance routineprovided by the operator of the Server 200. Different maintenanceroutines may operate at different priority levels and across differenttime scales. For example, the value of the sensor data point 340 or thecalculated data point 345 that is to be monitored may be as simple astemperature of Room 100, or as complex as maximizing photosynthesis viathe NDVI calculated data point 345.

At block 810, the allowed range of the sensor data point 340 or thecalculated data point 345 may be obtained from the range 375. At block815, a determination may be made regarding whether or not the sensordata point 340 or the calculated data point 345 is outside the range375. If affirmative at block 815, a list of one or more control devices105 associated with the sensor data point 340 or the calculated datapoint 345 may be obtained from the vector association array 380 at block820. The one or more control devices 106 may be ranked according to thedirectional response of the sensor data point 340 or the calculated datapoint 345 to each control device 105. Accordingly, one or more topranked control devices 105 may be selected and/or prioritized.

At block 825, if more than one top ranked control device 105 is selectedand/or prioritized, the load among the control devices 105 may bebalanced. For example, in a scenario in which the temperature is toohigh, there may be multiple control devices 105 in the form of lights.Accordingly, the reduction in power for dimming the lights to reduceheat may be spread among the lights. At block 830, the limit maintainerroutine may initiate an iteration of blocks 835 to 845 for each of theone or more selected or prioritized control devices 105 of block 820.

At block 835, one or more setting values for the one or more controldevices 105 of block 820 may be incremented up or down. At block 840,the one or more incremented setting values of block 835 may be output tothe device control routine 700. At block 899, the limit maintainerroutine 800 may conclude, may return to a starting state, and/or returnto a process that spawned the limit maintainer routine 800.

FIG. 9 is a flow diagram of an example process for implementing the userprogram generation routine 900 of a plant production feedback controlloop. At block 905, the user program generation routine 900 may initiatean iteration of blocks 910 to 955 for a user, who may be identified bythe user record 390. At block 910, an identifier of a site may bereceived and stored, such as in the site record 305. At block 915, anidentifier of a room may be received and stored, such as in the roomrecord 310. At block 920, an identifier of a plant may be received andstored, such as in the plant record 315.

At block 925, the user program generation routine 900 may initiate aniteration of blocks 930 to 950 for each site, each room, and each plantcombination that the user may designate. At block 930, a target may bereceived, such as the sensor data point 340 and/or the calculated datapoint 345. The sensor data point 340 and/or the calculated data point345 may be stored as the target 365. At block 935, a time interval forthe target 365 may be received and stored as the time interval 355. Atblock 940, a target path or means to obtain the target 365 via settingsof the control device 105 or the desired Sensor data point 340 may beobtained and stored as the target path 385. In various embodiments, auser may designate the target path 385 via a selection made through themain control routine 400. However, such a designation step may beoptional.

At block 945, the values of blocks 930, 935, and 940 may be stored asthe user program 395. However, as an alternative to user programgeneration routine 900, a user may utilize the API 265 to control theserver 200 and the routines that are executed by the server 200. Forexample, by using the API 265, the user may call methods and dataclasses to directly control a control device 105, to receive informationfrom a sensor 110, and to initiate execution of one or more routines bythe server 200. In this way, the API 265 may be used to execute theuser's own programs, as written in the user's programming languages, forthe user's compatible devices, utilizing one or more of the routines,services, and data classes made available by the routines executed bythe server 200

In various embodiments, the data points, target values, input variablevalues, output variable values, and other values that are described withrespect to the software routines illustrated in FIGS. 4-9 may beabsolute values or delta values. A delta value is a value that measureschange between two states, such as between an initial state and a finalstate. For example, a target value that is received may be a specifictemperature or a specific change in temperature. In another example, acalculated data point may be a specific growth rate for a plant or aspecific increase in the growth rate for the plant. In a furtherexample, the optimum input variable value may be an optimum wateringrate or an optimum decrease in watering rate. According, the softwareroutines of the plant production feedback control loop as described inthe various embodiments may receive, process, and generate absolutevalues and/or delta values for optimizing plant growth.

Conclusion

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims. Further, any specific numbers noted herein areonly examples, and alternative implementations may employ differingvalues or ranges.

What is claimed is:
 1. One or more non-transitory computer-readablemedia storing computer-executable instructions that upon execution causeone or more processors to perform acts comprising: executing ameasurement routine for a plant that is located in least one of a roomor a site, the measurement routine generating a calculated data pointfor a growth of the plant; executing a feedback analysis routine for theplant across a time interval to develop an optimum input variable valuefor the growth of the plant; obtaining a target value for a sensor datapoint of a sensor that monitors a condition affecting the growth of theplant or the calculated data point for the growth of the plant;determining one or more control devices that most strongly correlatewith the target value based on at least one of the optimum inputvariable value or a vector association array, the one or more controldevices controlling conditions that affect the growth of the plant;ascertaining at least one control device setting value for the one ormore control devices based on a target path for achieving the targetvalue for the sensor data point or the calculated data point; andexecuting a device control routine to command the one or more controldevices to implement the at least one control device setting value. 2.The one or more non-transitory computer-readable media of claim 1,wherein the acts further comprise executing a limit maintainer routineon the one or more control devices to maintain the sensor data pointwithin a range.
 3. The one or more non-transitory computer-readablemedia of claim 2, wherein the executing the limit maintainer routineincludes: obtaining a range for a sensor data point value of a sensorthat monitors the condition affecting the growth of the plant; obtaininga list of one or more control devices associated with the sensor inresponse to the sensor data point value being outside of the range;perform an incremental increase or an incremental decrease of a controldevice setting value for a control device; and outputting the controlsetting value to the control device via a device control routine.
 4. Theone or more non-transitory computer-readable media of claim 3, furthercomprising load balancing a load between a plurality of control deviceswhen the list includes the plurality of control devices.
 5. The one ormore non-transitory computer-readable media of claim 1, furthercomprising: receiving the target value for the plant that is located inat least one of the room or the site; receiving the time interval forthe target value; receiving the target path for obtaining the targetvalue via at least one control device setting of the one or more controldevices or the sensor data point; and storing the target value, the timeinterval, and the target path in a data store.
 6. The one or morenon-transitory computer-readable media of claim 1, wherein the sensor isa temperature sensor, a gas composition sensor, a pH sensor, a windspeed sensor, an air pressure sensor, a light sensor, a camera, a motionsensor, a flow sensor, a position sensor, an acoustic sensor, anultrasonic acoustic sensor, an acceleration sensor, a magnetic sensor, acontact sensor, a tilt sensor, a flex sensor, an electromagnetic field(EMF) sensor, a radiation sensor, or a circuit sensor.
 7. The one ormore non-transitory computer-readable media of claim 1, wherein the oneor more control devices include an artificial light, a relay, asolenoid, a motor, a dimmer, a CO₂ emitter, a CO₂ absorber, a gas valve,a liquid valve, a louver, a heater, an air conditioner, a fan, ahumidifier, a dehumidifier, an actuator, an acoustic emitter, a switch,a circuit breaker, a liquid pump, or a gas pump.
 8. The one or morenon-transitory computer-readable media of claim 1, wherein the executingthe measurement routine includes: storing a direct measurement receivedfrom a sensor as a sensor data point; retrieving a calculation formulathat corresponds to the sensor data point; and obtaining the calculateddata point for the growth of the plant by performing a calculation onthe sensor data point based on the calculation formula.
 9. The one ormore non-transitory computer-readable media of claim 1, wherein theexecuting the feedback analysis routine includes: retrieving acorresponding sensor data point or a calculated data point for eachdesired output variable associated with the growth of the plant, eachsensor data point or each calculated data point being an input variablein a range of input variables for a time interval; determining acorresponding input variable value for each input variable that producesa maximum output value for a corresponding desired output variable usinga brute force algorithm or a machine learning algorithm; storing thecorresponding input variable value that is determined for each inputvariable as an optimum input variable value for a corresponding maximumoutput value; determining a directional response between each inputvariable value and the corresponding maximum output value; and storingeach directional response with a corresponding optimum input variablevalue in the vector association array.
 10. The one or morenon-transitory computer-readable media of claim 1, wherein the executinga device control routine include: receiving a corresponding controldevice setting value for each of the one or more control devices;obtaining a control device type of each control device that correspondsto each control device setting value; setting the corresponding controldevice setting value for each of the one or more control devices;requesting device values from each of the one or more control devicesthat correspond to control device setting values; and storing a devicevalue that is received from a control device as a control device datapoint for a corresponding control device setting value of the controldevice in response to a determination that the device value is anexpected value.
 11. The one or more non-transitory computer-readablemedia of claim 10, further comprising generating an error message inresponse to a failure to receive an additional device value thatcorresponds to an additional control device setting value or in responseto a determination that the additional device value is an unexpectedvalue.
 12. The one or more non-transitory computer-readable media ofclaim 10, wherein the one or more control device includes an artificiallight, and wherein a control device setting value for the artificiallight controls at least one of a light level or a light spectrum that isoutputted by the artificial light for the plant.
 13. The one or morenon-transitory computer-readable media of claim 1, wherein thecalculated data point is a maximized photosynthesis of the plant that isobtained via a Normalized Difference Vegetation Index (NDVI) calculationon near-infrared and visible spectrum images as obtained from values ofa plurality of sensor data points.
 14. The one or more non-transitorycomputer-readable media of claim 1, wherein the plant is located in aroom, and the calculated data point is a temperature of the room. 15.The one or more non-transitory computer-readable media of claim 1,wherein the target value is moisture level, a temperature level, a lightlevel, a photosynthesis rate, a plant growth rate, or a plant esterproduction rate.
 16. A system, comprising: one or more processors; andmemory including a plurality of computer-executable components that areexecutable by the one or more processors to perform a plurality ofactions, the plurality of actions comprising: generating a calculateddata point for a growth of a plant; developing an optimum input variablevalue for the growth of the plant; obtaining a target value for a sensordata point of a sensor that monitors a condition affecting the growth ofthe plant or the calculated data point for the growth of the plant;determining one or more control devices that most strongly correlatewith the target value based on at least one of the optimum inputvariable value or a vector association array, the one or more controldevices controlling conditions that affect the growth of the plant;ascertaining at least one control device setting value for the one ormore control devices based on a target path for achieving the targetvalue for the sensor data point or the calculated data point; andcommanding the one or more control devices to implement the at least onecontrol device setting value.
 17. The system of claim 16, wherein theplurality of actions further comprise maintaining the sensor data pointwithin a range by performing a limit maintainer routine that includes:obtaining a range for a sensor data point value of a sensor thatmonitors the condition affecting the growth of the plant; obtaining alist of one or more control devices associated with the sensor inresponse to the sensor data point value being outside of the range; loadbalancing a load between a plurality of control devices when the listincludes the plurality of control devices; perform an incrementalincrease or an incremental decrease of a control device setting valuefor a control device; and outputting the control setting value to thecontrol device via a device control routine.
 18. The system of claim 16,wherein the generating the calculated data point comprises executing ameasurement routine that includes: storing a direct measurement receivedfrom a sensor as a sensor data point; retrieving a calculation formulathat corresponds to the sensor data point; and obtaining the calculateddata point for the growth of the plant by performing a calculation onthe sensor data point based on the calculation formula.
 19. The systemof claim 16, wherein the developing the optimum input variable comprisesexecuting a feedback analysis routine that includes: retrieving acorresponding sensor data point or a calculated data point for eachdesired output variable associated with the growth of the plant, eachsensor data point or each calculated data point being an input variablein a range of input variables for a time interval; determining acorresponding input variable value for each input variable that producesa maximum output value for a corresponding desired output variable usinga brute force algorithm or a machine learning algorithm; storing thecorresponding input variable value that is determined for each inputvariable as an optimum input variable value for a corresponding maximumoutput value; determining a directional response between each inputvariable value and the corresponding maximum output value; and storingeach directional response with a corresponding optimum input variablevalue in the vector association array.
 20. The system of claim 16,wherein the commanding the one or more control devices comprisesexecuting a device control routine that include: receiving acorresponding control device setting value for each of the one or morecontrol devices; obtaining a control device type of each control devicethat corresponds to each control device setting value; setting thecorresponding control device setting value for each of the one or morecontrol devices; requesting device values from each of the one or morecontrol devices that correspond to control device setting values; andstoring a device value that is received from a control device as acontrol device data point for a corresponding control device settingvalue of the control device in response to a determination that thedevice value is an expected value; and executing a limit maintainerroutine on the one or more control devices to maintain the sensor datapoint within a range.
 21. A computer-implemented method, comprising:executing a measurement routine for a plant that is located in least oneof a room or a site, the measurement routine generating a calculateddata point for a growth of the plant; executing a feedback analysisroutine for the plant across a time interval to develop an optimum inputvariable value for the growth of the plant; obtaining a target value fora sensor data point of a sensor that monitors a condition affecting thegrowth of the plant or the calculated data point for the growth of theplant; determining one or more control devices that most stronglycorrelate with the target value based on at least one of the optimuminput variable value or a vector association array, the one or morecontrol devices controlling conditions that affect the growth of theplant; ascertaining at least one control device setting value for theone or more control devices based on a target path for achieving thetarget value for the sensor data point or the calculated data point;commanding the one or more control devices to implement the at least onecontrol device setting value; and executing a limit maintainer routineon the one or more control devices to maintain the sensor data pointwithin a range.
 22. The computer-implemented method of claim 21, whereinthe executing the measurement routine includes: storing a directmeasurement received from a sensor as a sensor data point; retrieving acalculation formula that corresponds to the sensor data point; andobtaining the calculated data point for the growth of the plant byperforming a calculation on the sensor data point based on thecalculation formula.
 23. The computer-implemented method of claim 21,wherein the executing the feedback analysis routine includes: retrievinga corresponding sensor data point or a calculated data point for eachdesired output variable associated with the growth of the plant, eachsensor data point or each calculated data point being an input variablein a range of input variables for a time interval; determining acorresponding input variable value for each input variable that producesa maximum output value for a corresponding desired output variable usinga brute force algorithm or a machine learning algorithm; storing thecorresponding input variable value that is determined for each inputvariable as an optimum input variable value for a corresponding maximumoutput value; determining a directional response between each inputvariable value and the corresponding maximum output value; and storingeach directional response with a corresponding optimum input variablevalue in the vector association array.
 24. The computer-implementedmethod of claim 21, wherein the executing a device control routineinclude: receiving a corresponding control device setting value for eachof the one or more control devices; obtaining a control device type ofeach control device that corresponds to each control device settingvalue; setting the corresponding control device setting value for eachof the one or more control devices; requesting device values from eachof the one or more control devices that correspond to control devicesetting values; and storing a device value that is received from acontrol device as a control device data point for a correspondingcontrol device setting value of the control device in response to adetermination that the device value is an expected value.
 25. Thecomputer-implemented method of claim 21, wherein the executing the limitmaintainer routine includes: obtaining a range for a sensor data pointvalue of a sensor that monitors the condition affecting the growth ofthe plant; obtaining a list of one or more control devices associatedwith the sensor in response to the sensor data point value being outsideof the range; load balancing a load between a plurality of controldevices when the list includes the plurality of control devices; performan incremental increase or an incremental decrease of a control devicesetting value for a control device; and outputting the control settingvalue to the control device via a device control routine.